Foundation Models

OpenAI’s flagship general-purpose model for complex reasoning, coding, multimodal understanding, and agentic tool use.

Anthropic’s highest-capability Claude tier for deep reasoning, long-context analysis, coding, and careful instruction following.

Anthropic’s balanced Claude tier — strong quality at lower latency and cost than Opus, widely used for production agents and coding.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

DeepSeek’s MoE general model — strong open-weight performance on coding and knowledge tasks with competitive API pricing.

DeepSeek’s reasoning-focused model trained with reinforcement learning for multi-step math, science, and coding problem solving.

Alibaba’s Qwen3 generation — strong multilingual open models spanning chat, reasoning modes, coding, and multimodal variants.

Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.

OpenAI’s flagship general-purpose model for complex reasoning, coding, multimodal understanding, and agentic tool use.

Anthropic’s highest-capability Claude tier for deep reasoning, long-context analysis, coding, and careful instruction following.

Anthropic’s balanced Claude tier — strong quality at lower latency and cost than Opus, widely used for production agents and coding.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

Google’s fast, cost-efficient Gemini tier for high-throughput chat, classification, and multimodal apps where latency matters.

Mistral’s flagship large model for enterprise reasoning, multilingual chat, and function calling via La Plateforme and cloud partners.

xAI’s Grok model family — real-time oriented assistants with strong coding and reasoning, available via xAI API and consumer surfaces.

Cohere’s Command R+ model optimized for retrieval-augmented generation, enterprise search, and multilingual business assistants.

DeepSeek’s MoE general model — strong open-weight performance on coding and knowledge tasks with competitive API pricing.

DeepSeek’s reasoning-focused model trained with reinforcement learning for multi-step math, science, and coding problem solving.

Alibaba’s Qwen3 generation — strong multilingual open models spanning chat, reasoning modes, coding, and multimodal variants.

Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.

Mistral’s sparse Mixture-of-Experts open models (e.g. Mixtral 8x7B / 8x22B) — efficient high-quality text generation for self-hosting.

Microsoft’s Phi family of small language models — high capability per parameter for on-device, edge, and cost-sensitive deployments.

OpenAI’s flagship general-purpose model for complex reasoning, coding, multimodal understanding, and agentic tool use.

Anthropic’s highest-capability Claude tier for deep reasoning, long-context analysis, coding, and careful instruction following.

Anthropic’s balanced Claude tier — strong quality at lower latency and cost than Opus, widely used for production agents and coding.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

DeepSeek’s MoE general model — strong open-weight performance on coding and knowledge tasks with competitive API pricing.

DeepSeek’s reasoning-focused model trained with reinforcement learning for multi-step math, science, and coding problem solving.

Alibaba’s Qwen3 generation — strong multilingual open models spanning chat, reasoning modes, coding, and multimodal variants.

Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.

Mistral’s flagship large model for enterprise reasoning, multilingual chat, and function calling via La Plateforme and cloud partners.

xAI’s Grok model family — real-time oriented assistants with strong coding and reasoning, available via xAI API and consumer surfaces.

Cohere’s Command R+ model optimized for retrieval-augmented generation, enterprise search, and multilingual business assistants.

Microsoft’s Phi family of small language models — high capability per parameter for on-device, edge, and cost-sensitive deployments.

OpenAI’s flagship general-purpose model for complex reasoning, coding, multimodal understanding, and agentic tool use.

Anthropic’s highest-capability Claude tier for deep reasoning, long-context analysis, coding, and careful instruction following.

Anthropic’s balanced Claude tier — strong quality at lower latency and cost than Opus, widely used for production agents and coding.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

DeepSeek’s MoE general model — strong open-weight performance on coding and knowledge tasks with competitive API pricing.

DeepSeek’s reasoning-focused model trained with reinforcement learning for multi-step math, science, and coding problem solving.

Alibaba’s Qwen3 generation — strong multilingual open models spanning chat, reasoning modes, coding, and multimodal variants.

Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.

Mistral’s flagship large model for enterprise reasoning, multilingual chat, and function calling via La Plateforme and cloud partners.

Mistral’s sparse Mixture-of-Experts open models (e.g. Mixtral 8x7B / 8x22B) — efficient high-quality text generation for self-hosting.

xAI’s Grok model family — real-time oriented assistants with strong coding and reasoning, available via xAI API and consumer surfaces.

Microsoft’s Phi family of small language models — high capability per parameter for on-device, edge, and cost-sensitive deployments.

OpenAI’s flagship general-purpose model for complex reasoning, coding, multimodal understanding, and agentic tool use.

Anthropic’s highest-capability Claude tier for deep reasoning, long-context analysis, coding, and careful instruction following.

Anthropic’s balanced Claude tier — strong quality at lower latency and cost than Opus, widely used for production agents and coding.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

Google’s fast, cost-efficient Gemini tier for high-throughput chat, classification, and multimodal apps where latency matters.

Alibaba’s Qwen3 generation — strong multilingual open models spanning chat, reasoning modes, coding, and multimodal variants.

Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

OpenAI’s flagship general-purpose model for complex reasoning, coding, multimodal understanding, and agentic tool use.

Anthropic’s highest-capability Claude tier for deep reasoning, long-context analysis, coding, and careful instruction following.

Anthropic’s balanced Claude tier — strong quality at lower latency and cost than Opus, widely used for production agents and coding.

Google’s Pro-class Gemini model for advanced reasoning and native multimodal workloads across text, images, audio, and long context.

Google’s fast, cost-efficient Gemini tier for high-throughput chat, classification, and multimodal apps where latency matters.

Alibaba’s Qwen3 generation — strong multilingual open models spanning chat, reasoning modes, coding, and multimodal variants.

Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.

Google’s fast, cost-efficient Gemini tier for high-throughput chat, classification, and multimodal apps where latency matters.

Mistral’s sparse Mixture-of-Experts open models (e.g. Mixtral 8x7B / 8x22B) — efficient high-quality text generation for self-hosting.

Microsoft’s Phi family of small language models — high capability per parameter for on-device, edge, and cost-sensitive deployments.